Edit the sixteen color-value variables, and create your own syntax highlighter colorscheme. When using Base16 Builder as a building tool, you can, like Solarized, make colorschemes that come in a light – and in a dark background version.
The Sass variables that are used here in this 'Colorscheme Lab', are basically the same that are used in the template.erb files by Base16 Builder, so after creating a colorscheme with this lab, converting to many application formats for syntax highlighting comes within reach.

• Atelier Schemes
• Base16 Builder

A CodePen by Bram de Haan

Last Edited: Mar 30, 2026 @ 7:45AM PST · Supports iOS versions 15.0 - 26.3

_{Make sure you are always using up-to-date guides to ensure full compatibility. The official SideStore documentation can be found here in case anything changes. For additional information and credits, scroll to the bottom of this page.}

Important • iOS 26.4 broke the method used by SideStore and this tutorial to refresh apps. A new guide will come soon for people on iOS 26.4 once the necessary tools are available.

👋 Hello again, r/sideloaded!

One Paragraph of project description goes here

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Script Hook RDR2

• Download: http://www.dev-c.com/rdr2/scripthookrdr2/
• Installation: Copy the contents of the bin folder directly into your main RDR2 root directory (where RDR2.exe is located).

Lenny Mod Loader

• Download: https://www.rdr2mods.com/downloads/rdr2/tools/76-lennys-mod-loader-rdr/

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

• set -e, -u, -x, -o pipefail
• set -e
• set -x
• set -u
• set -o pipefail
• Setting IFS
• Original Reference